🚦Test Design
To ensure the success of your A/B tests with ABConvert, follow these best practices when designing a test.
A/B testing is a powerful method for optimizing your store performance by comparing two or more variations of a page, product, price, or more. To ensure the success of your A/B tests with ABConvert, follow these best practices when designing a test.
1. Define a Clear Hypothesis
Before starting a test, it's essential to have a clear understanding of what you want to achieve. Whether you're testing price sensitivity, content engagement, or shipping options, your goals should be specific and measurable.
Start with a specific question or problem you want to address.
For example:
"Will increasing the free shipping threshold lead to a higher average order value?"
"Will changing the product images on the product detail page increase add-to-cart rates?"
"Will redirecting visitors from a specific ad to a dedicated landing page improve conversion rates?"
Formulate a testable hypothesis.
This should be a clear statement about what you expect to happen, i.e. your objective. For example:
"Increasing the free shipping threshold from $50 to $75 will result in a higher average order value."
"Using lifestyle images instead of product-only images on the product detail page will increase add-to-cart rates."
"Visitors coming from Facebook ads who are redirected to a landing page tailored to that ad campaign will have higher conversion rates than those sent to the general product page."
Clearly defining your hypothesis will help you design better experiments and determine what metrics to focus on during analysis.
2. Choose the Right Test Type
ABConvert supports various test types:
Price Test: Use this to experiment with different product prices. You can test individual products or run store-wide price adjustments.
Shipping Test: This allows you to test variations in shipping rates and free shipping thresholds.
URL Redirect Test: This enables you to test the effectiveness of sending traffic to different landing pages or PDPs.
Template Tests: Compare page layouts to see effects on key metrics.
Theme Test: Use this to compare the performance of different Shopify themes.
Select the test type that aligns with your goals. For example, if you want to understand how price affects conversion rates, use the Price Test.
3. Set Up Proper Traffic Splits
Ensure that you allocate traffic appropriately between test groups. ABConvert allows you to set traffic splits for up to five groups. A common practice is to start with a 50/50 split for two variations (control vs. variant) and adjust based on the number of variations being tested.
For more advanced targeting, you can filter traffic by country or audience (e.g., new vs. returning customers), ensuring that the right audience sees each variation.
Learn more about advanced targeting here:
4. Use Advanced Filtering Options if Needed
ABConvert offers advanced filtering options such as traffic source and country-based filters. These allow you to run tests on specific segments of your audience:
Traffic Source Filters: Target visitors based on their traffic source (e.g., Google Ads or Facebook campaigns).
Learn more about UTM (traffic source) filter here:
Country Filters: Run tests in specific countries while showing the original version in other regions.
This ensures that your tests are relevant to specific user groups and helps in gathering more actionable insights.
5. Check Your Test After Launch
Always check your tests after launching them. ABConvert provides multiple ways to preview tests:
Use the preview URL from the analytics dashboard.
Add
/?preview=0or/?preview=1to the end of the URL to view how each group will experience the test.
This step helps ensure that everything works as expected across different devices and browsers.
For more details, refer to
6. Monitor Key Metrics
Once your test is live, monitor key performance metrics such as:
Conversion rates
Average order value (AOV)
Revenue
ABConvert provides detailed analytics dashboards where you can track these metrics in real-time.
7. Ensure Statistical Significance
To make informed decisions based on your A/B test results, ensure that your test reaches statistical significance. ABConvert recommends running tests until you have at least 10,000 views and 200 orders in total. This ensures that your results are not due to random chance but reflect real user behavior.
The Statistical Significance section in ABConvert’s analytics dashboard provides a quick and easy way to see whether your test results are statistically significant.
Check out this article for more details:
Give the test enough time to collect meaningful data. Avoid making changes to the test or the website during the testing period, as this can skew results.
8. Analyze Results and Draw Conclusions
After completing an A/B test, refer back to your hypothesis and thoroughly analyze the results using ABConvert’s analytics dashboard:
Compare your target metric such as: conversion rates, across different variations.
Look at secondary metrics like average order value or profit per visitor.
Use insights from the test to decide which version to implement to your store, or inform future optimization efforts.
If needed, run follow-up tests based on what you learned from the initial experiment.
Learn more about managing your tests:
By following these best practices when designing A/B tests with ABConvert, you'll be able to gather actionable insights that can significantly improve your store's performance across pricing strategies, content engagement, shipping options, and more.
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